Embodiments of the present disclosure are generally related to grouping, enrichment, and presentation of data items accessed from one or more databases, and specifically to grouping, enrichment, and presentation of trade-related data items.
Detection of the occurrence of risky or unauthorized trading, and/or other undesirable behavior occurring within a business is a highly important, but oftentimes challenging task. Trader oversight may be useful for regulatory authorities seeking to make sure traders at the business are complying with laws or regulations. Risky or unauthorized trading may result in significant financial losses to the business and/or additional financial consequences such as penalties paid to regulators.
Detection of risky or unauthorized trading may be performed through the examination of trades performed by traders over time. Previously, determination and identification of risky or unauthorized trading through the examination of trades was a labor intensive task. For example, in an investigation of risky or unauthorized trading, an analyst may have had to pore through numerous collections of data (e.g., trading logs and other trade-related information) comprising hundreds of thousands, millions, tens of millions, hundreds of millions, or even billions of data items, manually discern patterns and perform analyses to gain additional context, and compile any information gleaned from such analyses. The analyst may have to make many decisions regarding selection of electronic data items within an electronic collection of data. Determination and selection of relevant data items within such collections of data may be extremely difficult for the analyst. In addition, such collections of data may consume significant storage and/or memory, and the processing thereof (for example, having an analyst using a computer to sift and/or search through huge numbers of data items) may be extremely inefficient and consume significant processing and/or memory resources.
In some instances related electronic data items may be clustered and stored in an electronic data store. Even when electronic data items are clustered, however, the electronic collection of data may include hundreds of thousands, millions, tens of millions, hundreds of millions, or even billions of clusters of data items. As with individual data items, determination and selection of relevant clusters of data items within such a collection of data may be extremely difficult for the analyst. Further, processing and presenting such clusters of data items in an efficient way to an analyst may be a very challenging task. The data should be presented to the analyst in a way that makes it easy for the analyst to interpret and arrive at conclusions over the potentially risky trading behavior.